Quantitative research is a method of collecting and analysing numerical data to identify patterns, measure variables, and test relationships. It answers questions such as “how much?”, “how many?”, “how often?”, and “what is the relationship between variables?” by using structured tools like surveys, experiments, or statistical analysis to produce objective, generalisable results. Here we will look at the primary tool; surveys or questionnaires.
quantitative = breadth and measurement, qualitative = depth and understanding
What it answers:
How many people came to my gig?
What percentage of my audience is under 25?
How often do fans stream my songs in a month?
What is the average ticket spend for my shows?
Why it matters in music:
Helps you track your growth (e.g. streams, sales, followers).
Shows funders or venues your reach with solid evidence.
Helps identify which platforms, songs, or events perform best.
Lets you compare across time (before and after a release/tour)
Common quantitative tools for musicians:
- Streaming and distribution platforms such as The Orchard, Ditto, Songtradr, Sentric, Spotify for Artists, Apple Music for Artists.
Social media insights (follower demographics, reach, engagement) and Chartmetric analytics.
Ticketing platforms (sales numbers, audience location).
Surveys with tick-box questions (age ranges, frequency of attendance).
Quick tip: Always ask yourself, “what decision will this number help me make?” Don’t just collect stats for the sake of it.
Quantitative Studies and Data-Driven Research on Women in Music
Vick Bain – Counting the Music Industry (2019)
One of the few systematic, large-scale quantitative analyses of gender in the UK music business of label rosters and publishing catalogues. Demonstrates how quantitative audits expose structural inequalities.
Annenberg Inclusion Initiative (US) – Inclusion in the Recording Studio? (annual reports from 2012)
US Billboard dataset: counts of artists, songwriters, producers, and Grammy nominees. Highly statistical, chart-based, and longitudinal.
UK Music – Workforce Diversity Survey (bi-annual reports from 2016)
Large-scale quantitative data on gender, ethnicity, disability, pay, seniority, and employment type. A survey-based methodology offering year-on-year trend analysis.
Why Not Her? (2020 – 2025)
Quantitative analyses revealing gender and racial imbalances in Irish and UK radio airplay, using track-level data to highlight disparities in playlist exposure.
Arts Council England – Equality, Diversity & the Creative Case (annual monitoring reports)
Not music-specific but provides robust quantitative workforce, leadership, and participation data across NPOs and music organisations.
Stahl, M. & Meier, L. – studies on gender and labour in music (various papers)
Selected papers include quantitative examinations of labour segmentation, income distribution, and structural inequalities in music work.
Women in Music Canada – Annual Reports (from 2015 onwards)
Offer quantitative breakdowns of industry roles, demographic representation, and perceived career barriers across the Canadian music sector.
Swedish Music Industry Gender Equality Reports (from 2007 onwards)
Among the strongest international datasets. Provide detailed quantitative measures of gender representation in music production, festival programming, and industry leadership.
Maria Hanáček – festival gender quota analyses (various European datasets)
Employs quantitative festival-line-up data across countries to assess the effectiveness and impact of gender-quota initiatives.
Chartmetric / Spotify Data Investigations
Data-driven analyses of gender in playlist inclusion, genre categorisation, chart performance, and algorithmic visibility. Primarily through their “Make Music Equal” initiative, now has pronoun data on more than 1 million artists from 230 countries.
The Inclusion Initiative (LSE) – Creative Industries Gender & Pay-Gap Datasets
Not music-specific but includes large-sample econometric models on gendered career outcomes including pay-gap analyses that can inform music-sector comparisons.
TOP TIPS FOR SURVEY DESIGN
Define Your Goal: Clearly state what you want to learn from the survey to maintain focus.
Know Your Audience: Tailor your language and questions to be easily understood by your target respondents.
Plan Your Question Types: Use closed-ended questions for easier analysis where possible, but also consider open-ended questions for detailed feedback (this is qualitative).
You Can Use or Adapt Questions: Used in other questionnaires/other research or create your own questions
Keep it Simple: Use clear, straightforward language that will be familiar and avoid jargon, technical terms, or industry-specific language.
Keep it as Short as Possible: Brevity will aid comprehension and don’t ask two questions in one.
Be Specific: Ask about one topic or aspect at a time to avoid confusing respondents and get focused answers.
Maintain Neutrality: Phrase questions in a neutral way to prevent biasing responses.
Offer Balanced Choices: Provide a range of answer options that cover the spectrum of possible responses, avoiding “absolutes” like “always” or “never”.
Include a “N/A” or “Don’t Know” Option: This can prevent respondents from feeling forced to give an answer that doesn’t apply to them.
Preview Your Survey: Look at the survey from a respondent’s point of view to check for any flow or clarity issues.
Test Your Survey: Have others take the survey to identify any errors, confusing questions, or technical problems before distributing it.
Keep it Concise: A shorter survey leads to better completion rates.
